Journal of Machine Learning Research最新文献_第10页

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes. 支持向量危险机：一个计算过程框架，用于学习审查结果的风险评分。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 Epub Date: 2016-08-01

Yuanjia Wang, Tianle Chen, Donglin Zeng

{"title":"Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.","authors":"Yuanjia Wang, Tianle Chen, Donglin Zeng","doi":"","DOIUrl":"","url":null,"abstract":"Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"17 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71434774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gradients weights improve regression and classification 梯度权重改进了回归和分类

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI: 10.5555/2946645.2946667

KpotufeSamory, BoulariasAbdeslam, SchultzThomas, KimKyoungok

引用次数: 2

Fused lasso approach in regression coefficients clustering 回归系数聚类的融合套索方法

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI: 10.5555/2946645.3007066

TangLu

引用次数: 4

Input output kernel regression 输入输出核回归

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI: 10.5555/2946645.3053458

BrouardCéline, SzafranskiMarie, D'Alché-BucFlorence

引用次数: 1

A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces. 发散模型空间中支持向量机的一致信息准则。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01

Xiang Zhang, Yichao Wu, Lan Wang, Runze Li

引用次数: 0

Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration. 回归系数聚类的融合Lasso方法——数据集成中参数异质性的学习。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01

Lu Tang, Peter X K Song

{"title":"Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration.","authors":"Lu Tang, Peter X K Song","doi":"","DOIUrl":"","url":null,"abstract":"As data sets of related studies become more easily accessible, combining data sets of similar studies is often undertaken in practice to achieve a larger sample size and higher power. A major challenge arising from data integration pertains to data heterogeneity in terms of study population, study design, or study coordination. Ignoring such heterogeneity in data analysis may result in biased estimation and misleading inference. Traditional techniques of remedy to data heterogeneity include the use of interactions and random effects, which are inferior to achieving desirable statistical power or providing a meaningful interpretation, especially when a large number of smaller data sets are combined. In this paper, we propose a regularized fusion method that allows us to identify and merge inter-study homogeneous parameter clusters in regression analysis, without the use of hypothesis testing approach. Using the fused lasso, we establish a computationally efficient procedure to deal with large-scale integrated data. Incorporating the estimated parameter ordering in the fused lasso facilitates computing speed with no loss of statistical power. We conduct extensive simulation studies and provide an application example to demonstrate the performance of the new method with a comparison to the conventional methods.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"17 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647925/pdf/nihms872528.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35531942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Objective Markov Decision Processes for Data-Driven Decision Support. 用于数据驱动决策支持的多目标马尔可夫决策过程。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 Epub Date: 2016-12-01

Daniel J Lizotte, Eric B Laber

引用次数: 0

Dimension-free concentration bounds on hankel matrices for spectral learning 用于谱学习的汉克尔矩阵的无维集中界

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI: 10.5555/2946645.2946676

DenisFrançois, GybelsMattias, HabrardAmaury

引用次数: 1

Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision. 利用远距离监督从临床试验报告中提取 PICO 句子

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01

Byron C Wallace, Joël Kuiper, Aakash Sharma, Mingxi Brian Zhu, Iain J Marshall

{"title":"Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision.","authors":"Byron C Wallace, Joël Kuiper, Aakash Sharma, Mingxi Brian Zhu, Iain J Marshall","doi":"","DOIUrl":"","url":null,"abstract":"Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method - supervised distant supervision (SDS) - that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"17 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graphical Models via Univariate Exponential Family Distributions. 通过单变量指数族分布建立图形模型

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2015-12-01

Eunho Yang, Pradeep Ravikumar, Genevera I Allen, Zhandong Liu

{"title":"Graphical Models via Univariate Exponential Family Distributions.","authors":"Eunho Yang, Pradeep Ravikumar, Genevera I Allen, Zhandong Liu","doi":"","DOIUrl":"","url":null,"abstract":"Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data. In this paper, we consider a general sub-class of graphical models where the node-wise conditional distributions arise from exponential families. This allows us to derive multivariate graphical model distributions from univariate exponential family distributions, such as the Poisson, negative binomial, and exponential distributions. Our key contributions include a class of M-estimators to fit these graphical model distributions; and rigorous statistical analysis showing that these M-estimators recover the true graphical model structure exactly, with high probability. We provide examples of genomic and proteomic networks learned via instances of our class of graphical models derived from Poisson and exponential distributions.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"16 ","pages":"3813-3847"},"PeriodicalIF":6.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4998206/pdf/nihms808903.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34398019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0